S. Makni, P. Ciuciu, J. Idier, and J. Poline, Joint detection-estimation of brain activity in functional MRI: a Multichannel Deconvolution solution, IEEE Transactions on Signal Processing, vol.53, issue.9, pp.3488-3502, 2005.
DOI : 10.1109/TSP.2005.853303

S. Makni, J. Idier, T. Vincent, B. Thirion, G. Dehaene-lambertz et al., A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI, NeuroImage, vol.41, issue.3, pp.941-969, 2008.
DOI : 10.1016/j.neuroimage.2008.02.017

URL : https://hal.archives-ouvertes.fr/cea-00333624

S. Ogawa, T. Lee, A. Kay, and D. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation., Proc. Natl
DOI : 10.1073/pnas.87.24.9868

K. Friston, Imaging neuroscience: Principles or maps?, Proc. Natl. Acad. Sci. USA, pp.796-802, 1998.
DOI : 10.1073/pnas.95.3.796

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC33800/pdf

R. Henson, C. Price, M. Rugg, R. Turner, and K. Friston, Detecting Latency Differences in Event-Related BOLD Responses: Application to Words versus Nonwords and Initial versus Repeated Face Presentations, NeuroImage, vol.15, issue.1, pp.83-97, 2002.
DOI : 10.1006/nimg.2001.0940

K. Friston, Statistical parametric mapping, Functional Neuroimaging : Technical Foundations, pp.79-93, 1994.
DOI : 10.1007/978-1-4615-1079-6_16

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.8.3745

N. Lange, Empirical and substantive models, the Bayesian paradigm, and meta???analysis in functional brain imaging, Human Brain Mapping, vol.5, issue.4, pp.259-263, 1997.
DOI : 10.1002/(SICI)1097-0193(1997)5:4<259::AID-HBM10>3.3.CO;2-#

M. S. Cohen, Parametric Analysis of fMRI Data Using Linear Systems Methods, NeuroImage, vol.6, issue.2, pp.93-103, 1997.
DOI : 10.1006/nimg.1997.0278

J. C. Rajapakse, F. Kruggel, J. M. Maisog, and D. Von-cramon, Modeling hemodynamic response for analysis of functional MRI time-series, Human Brain Mapping, vol.2, issue.4, pp.283-300, 1998.
DOI : 10.1002/(SICI)1097-0193(1998)6:4<283::AID-HBM7>3.0.CO;2-#

F. Kruggel and D. Y. Von-crammon, Modeling the hemodynamic response in single-trial functional MRI experiments, Magnetic Resonance in Medicine, vol.6, issue.4, pp.787-797, 1999.
DOI : 10.1002/(SICI)1522-2594(199910)42:4<787::AID-MRM22>3.0.CO;2-V

C. Genovese, A Bayesian Time-Course Model for Functional Magnetic Resonance Imaging Data, Journal of the American Statistical Association, vol.55, issue.451, pp.691-719, 2000.
DOI : 10.1006/nimg.1995.1023

C. Gössl, D. P. Auer, and L. Fahrmeir, Bayesian Spatiotemporal Inference in Functional Magnetic Resonance Imaging, Biometrics, vol.22, issue.2, pp.554-562, 2001.
DOI : 10.1111/j.0006-341X.2001.00554.x

M. Woolrich, M. Jenkinson, J. Brady, and S. Smith, Fully Bayesian Spatio-Temporal Modeling of FMRI Data, IEEE Transactions on Medical Imaging, vol.23, issue.2, pp.213-231, 2004.
DOI : 10.1109/TMI.2003.823065

F. A. Nielsen, L. K. Hansen, P. Toft, C. Goutte, N. Lange et al., Comparison of two convolution models for fMRI time series, Neuroimage, vol.5, p.473, 1997.

C. Goutte, F. A. Nielsen, and L. K. Hansen, Modeling the hemodynamic response in fMRI using smooth FIR filters, IEEE Transactions on Medical Imaging, vol.19, issue.12, pp.1188-1201, 2000.
DOI : 10.1109/42.897811

G. Marrelec, H. Benali, P. Ciuciu, M. Pélégrini-issac, and J. Poline, Robust Bayesian estimation of the hemodynamic response function in event-related BOLD fMRI using basic physiological information, Human Brain Mapping, vol.2, issue.1, pp.1-17, 2003.
DOI : 10.1002/hbm.10100

URL : https://hal.archives-ouvertes.fr/cea-00333748

P. Ciuciu, J. Poline, G. Marrelec, J. Idier, C. Pallier et al., Unsupervised robust nonparametric estimation of the hemodynamic response function for any fmri experiment, IEEE Transactions on Medical Imaging, vol.22, issue.10, pp.1235-1251, 2003.
DOI : 10.1109/TMI.2003.817759

URL : https://hal.archives-ouvertes.fr/cea-00333694

G. Marrelec, P. Ciuciu, M. Pélégrini-issac, and H. Benali, Estimation of the Hemodynamic Response in Event-Related Functional MRI: Bayesian Networks as a Framework for Efficient Bayesian Modeling and Inference, IEEE Transactions on Medical Imaging, vol.23, issue.8, pp.959-967, 2004.
DOI : 10.1109/TMI.2004.831221

URL : https://hal.archives-ouvertes.fr/cea-00333687

R. B. Buxton, E. C. Wong, and F. L. , Dynamics of blood flow and oxygenation changes during brain activation: The balloon model, Magnetic Resonance in Medicine, vol.77, issue.6, pp.855-864, 1998.
DOI : 10.1002/mrm.1910390602

K. J. Friston, A. Mechelli, R. Turner, and C. J. Price, Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics, NeuroImage, vol.12, issue.4, pp.466-477, 2000.
DOI : 10.1006/nimg.2000.0630

R. B. Buxton, K. U. , D. J. Dubowitz, and T. T. Liu, Modeling the hemodynamic response to brain activation, NeuroImage, vol.23, pp.220-233, 2004.
DOI : 10.1016/j.neuroimage.2004.07.013

J. Riera, J. Watanabe, I. Kazuki, M. Naoki, E. Aubert et al., A state-space model of the hemodynamic approach: nonlinear filtering of BOLD signals, NeuroImage, vol.21, issue.2, pp.547-567, 2004.
DOI : 10.1016/j.neuroimage.2003.09.052

T. Deneux and O. Faugeras, EEG-fMRI Fusion of Non-Triggered Data Using Kalman Filtering, 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., pp.6-9, 2006.
DOI : 10.1109/ISBI.2006.1625106

URL : https://hal.archives-ouvertes.fr/inria-00070260

K. E. Stephan, N. Weiskopf, P. M. Drysdale, P. A. Robinson, and K. J. Friston, Comparing hemodynamic models with DCM, NeuroImage, vol.38, issue.3, pp.387-401, 2007.
DOI : 10.1016/j.neuroimage.2007.07.040

URL : http://doi.org/10.1016/j.neuroimage.2007.07.040

J. Riera, J. Bosch, O. Yamashita, R. Kawashima, N. Sadato et al., fMRI activation maps based on the NN-ARx model, NeuroImage, vol.23, issue.2, pp.680-697, 2004.
DOI : 10.1016/j.neuroimage.2004.06.039

P. Baraldi, A. Manginelli, M. Maieron, D. Liberati, and A. Porro, An ARX model-based approach to trial by trial identification of fMRI-BOLD responses, NeuroImage, vol.37, issue.1, pp.189-201, 2007.
DOI : 10.1016/j.neuroimage.2007.02.045

L. A. Johnston, E. Duff, I. Mareels, and G. F. Egan, Nonlinear estimation of the BOLD signal, NeuroImage, vol.40, issue.2, pp.504-514, 2008.
DOI : 10.1016/j.neuroimage.2007.11.024

B. Thirion, G. Flandin, P. Pinel, A. Roche, P. Ciuciu et al., Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets, Human Brain Mapping, vol.22, issue.8, pp.678-693, 2006.
DOI : 10.1002/hbm.20210

. Poline, A new representation of fMRI data using anatomo-functional constraints, Proc. 8th HBM, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00615928

D. Lashkari and P. Golland, Exploratory fMRI Analysis without Spatial Normalization, 21st Proceedings of IPMI, 2009.
DOI : 10.1023/A:1007665907178

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2836541/pdf

W. Penny, Z. Ghahramani, and K. Friston, Bilinear dynamical systems, Philosophical Transactions of the Royal Society B: Biological Sciences, vol.21, issue.4, pp.983-993, 2005.
DOI : 10.1002/hbm.20000

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1854926

S. Makni, C. Beckmann, S. Smith, and M. Woolrich, Bayesian deconvolution fMRI data using bilinear dynamical systems, NeuroImage, vol.42, issue.4, pp.1381-1396, 2008.
DOI : 10.1016/j.neuroimage.2008.05.052

C. Beckmann and S. Smith, Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging, IEEE Transactions on Medical Imaging, vol.23, issue.2, pp.137-152, 2004.
DOI : 10.1109/TMI.2003.822821

W. D. Penny, N. Trujillo-barreto, and K. J. Friston, Bayesian fMRI time series analysis with spatial priors, NeuroImage, vol.24, issue.2, pp.350-362, 2005.
DOI : 10.1016/j.neuroimage.2004.08.034

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.110.6316

G. Flandin and W. D. Penny, Bayesian fMRI data analysis with sparse spatial basis function priors, NeuroImage, vol.34, issue.3, pp.1108-1125, 2007.
DOI : 10.1016/j.neuroimage.2006.10.005

L. M. Harrison, W. Penny, J. Daunizeau, and K. J. Friston, Diffusion-based spatial priors for functional magnetic resonance images, NeuroImage, vol.41, issue.2, pp.408-423, 2008.
DOI : 10.1016/j.neuroimage.2008.02.005

D. Available, G. Geman, and . Reynolds, Constrained restoration and the recovery of discontinuities, IEEE Trans. Pattern Anal. Mach. Intell, vol.14, issue.3, pp.367-383, 1992.

P. Charbonnier, L. Blanc-féraud, G. Aubert, and M. Barlaud, Deterministic edge-preserving regularization in computed imaging, IEEE Transactions on Image Processing, vol.6, issue.2, pp.298-311, 1997.
DOI : 10.1109/83.551699

F. Forbes and N. Peyrard, Hidden markov random field model selection criteria based on mean field-like approximations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.9, pp.1089-1101, 2003.
DOI : 10.1109/TPAMI.2003.1227985

M. Beal, Variational algorithms for approximate Bayesian inference, 2003.

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001.
DOI : 10.1109/34.969114

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.6806

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004.
DOI : 10.1109/TPAMI.2004.60

F. C. Jeng and J. W. Woods, Compound Gauss-Markov random fields for image estimation, IEEE Transactions on Signal Processing, vol.39, issue.3, pp.683-697, 1991.
DOI : 10.1109/78.80887

D. M. Higdon, Auxiliary Variable Methods for Markov Chain Monte Carlo with Applications, Journal of the American Statistical Association, vol.21, issue.442, pp.585-595, 1998.
DOI : 10.1080/01621459.1985.10477119

P. J. Green and S. Richardson, Hidden Markov models and desease mapping, J. Amer. Statist. Assoc, vol.97, issue.460, pp.1-16, 2002.

M. Smith, D. Pütz, L. Auer, and . Fahrmeir, Assessing brain activity through spatial bayesian variable selection, NeuroImage, vol.20, issue.2, pp.802-815, 2003.
DOI : 10.1016/S1053-8119(03)00360-4

URL : https://epub.ub.uni-muenchen.de/1697/1/paper_316.pdf

S. Fernández and P. J. Green, Modelling spatially correlated data via mixtures: a Bayesian approach, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.92, issue.4, pp.805-826, 2002.
DOI : 10.1111/1467-9868.00288

M. Woolrich, T. Behrens, C. Beckmann, and S. Smith, Mixture models with adaptive spatial regularization for segmentation with an application to FMRI data, IEEE Transactions on Medical Imaging, vol.24, issue.1, pp.1-11, 2005.
DOI : 10.1109/TMI.2004.836545

A. Barbu and S. Zhu, Generalizing Swendsen-Wang to sampling arbitrary posterior probabilities, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.27, issue.8, pp.1239-1253, 2005.
DOI : 10.1109/TPAMI.2005.161

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.454.965

M. Woolrich and T. Behrens, Variational bayes inference of spatial mixture models for segmentation, IEEE Transactions on Medical Imaging, vol.25, issue.10, pp.1380-1391, 2006.
DOI : 10.1109/TMI.2006.880682

M. Svensen, F. Kruggel, and D. Von-crammon, Probabilistic modeling of single-trial fMRI data, IEEE Transactions on Medical Imaging, vol.19, issue.1, pp.19-35, 2000.
DOI : 10.1109/42.832957

W. Ou and P. Golland, From Spatial Regularization to Anatomical Priors in fMRI Analysis, IPMI, Glenwood Springs, 2005.
DOI : 10.1007/11505730_8

S. Geman and D. Geman, Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images, IEEE Transaction on Pattern Analysis and Machine Intelligence, issue.6, pp.721-741, 1984.

D. Geman and C. Yang, Nonlinear image recovery with half-quadratic regularization, IEEE Transactions on Image Processing, vol.4, issue.7, pp.932-946, 1995.
DOI : 10.1109/83.392335

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.53.7349

T. Vincent, P. Ciuciu, and J. Idier, Spatial Mixture Modelling for the Joint Detection-Estimation of Brain Activity in fMRI, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, pp.325-328, 2007.
DOI : 10.1109/ICASSP.2007.366682

URL : https://hal.archives-ouvertes.fr/hal-00408628

N. Vaever-hartvig and J. Jensen, Spatial mixture modeling of fMRI data, Human Brain Mapping, vol.4, issue.4, pp.233-248, 2000.
DOI : 10.1002/1097-0193(200012)11:4<233::AID-HBM10>3.0.CO;2-F

D. Smith and M. Smith, Estimation of Binary Markov Random Fields Using Markov chain Monte Carlo, Journal of Computational and Graphical Statistics, vol.15, issue.1, pp.207-227, 2006.
DOI : 10.1198/106186006X97817

D. Poline and . Rivière, Cerebral mechanisms of word masking and unconscious repetition priming, Nat. Neurosci, vol.4, issue.7, pp.752-758, 2001.
URL : https://hal.archives-ouvertes.fr/hal-00349842

P. Ciuciu, S. Sockeel, T. Vincent, and J. Idier, Modelling the neurovascular habituation effect on fMRI time series, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.433-436, 2009.
DOI : 10.1109/ICASSP.2009.4959613

X. Meng and W. Wong, Simulating ratios of normalizing constants via a simple identity: a theoretical exploration, Statistica Sinica, vol.6, pp.831-860, 1996.

D. M. Higdon, J. E. Bowsher, V. E. Johnson, T. G. Turkington, D. R. Gilland et al., Fully Bayesian estimation of Gibbs hyperparameters for emission computed tomography data, IEEE Transactions on Medical Imaging, vol.16, issue.5
DOI : 10.1109/42.640741

A. Gelman and X. Meng, Simulating normalizing constants: from importance sampling to bridge sampling to path sampling, Statistical Science, vol.13, issue.2, pp.163-185, 1998.
DOI : 10.1214/ss/1028905934

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.44.183

L. Risser, J. Idier, and P. Ciuciu, Bilinear extrapolation scheme for fast estimation of 3D ising field partition function. Application to fMRI time course analysis, 16th Proc. IEEE ICIP, 2009.

L. Risser, T. Vincent, P. Ciuciu, and J. Idier, Robust Extrapolation Scheme for Fast Estimation of 3D Ising Field Partition Functions: Application to Within-Subject fMRI Data Analysis, 12thProc. MICCAI'09, ser, pp.975-983, 2009.
DOI : 10.1007/978-3-642-04268-3_120

URL : https://hal.archives-ouvertes.fr/cea-00470658

A. Shmuel, M. Augath, A. Oeltermann, and N. K. Logothetis, Negative functional MRI response correlates with decreases in neuronal activity in monkey visual area V1, Nature Neuroscience, vol.33, issue.4, pp.569-577, 2006.
DOI : 10.1038/nn1675

L. Risser, T. Vincent, and P. Ciuciu, Schéma d'extrapolation de fonctions de partition de champs de potts. application à l'analyse d'images en IRMf, Actes du 22 e colloque GRETSI, 2009.

J. Liu, Monte Carlo strategies in scientific computing, ser. Springer series in Statistics, 2001.

B. Thyreau, B. Thirion, G. Flandin, and J. Poline, Anatomofunctional description of the brain: a probabilistic approach, Proc. 31th Proc. IEEE ICASSP, pp.1109-1112, 2006.

. Poline, Probabilistic anatomo-functional parcellation of the cortex: how many regions, 11thProc. MICCAI, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00502805

K. Worsley, C. Liao, J. Aston, V. Petre, G. Duncan et al., A General Statistical Analysis for fMRI Data, NeuroImage, vol.15, issue.1, pp.1-15, 2002.
DOI : 10.1006/nimg.2001.0933

M. Woolrich, M. Jenkinson, J. M. Brady, and S. Smith, Constrained linear basis sets for HRF modelling using Variational Bayes, NeuroImage, vol.21, issue.4, pp.1748-1761, 2004.
DOI : 10.1016/j.neuroimage.2003.12.024

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.329.3397

S. M. Kay, Modern Spectral Estimation, 1988.

A. Trillon, J. Idier, and P. Peureux, Unsupervised Bayesian 3D reconstruction for non-destructive evaluation using gammagraphy, EUSIPCO, 2008.

C. Geyer and E. Thompson, Constrained monte carlo maximum likelihood for dependent data, J. Roy. Statist. Soc, vol.54, pp.657-699, 1992.

R. H. Swendsen and J. S. Wang, Nonuniversal critical dynamics in Monte Carlo simulations, Physical Review Letters, vol.58, issue.2, pp.86-88, 1987.
DOI : 10.1103/PhysRevLett.58.86

D. Smith and L. Fahrmeir, Spatial Bayesian Variable Selection With Application to Functional Magnetic Resonance Imaging, Journal of the American Statistical Association, vol.102, issue.478, pp.417-431, 2007.
DOI : 10.1198/016214506000001031

R. Edwards and A. Sokal, Generalization of the Fortuin-Kasteleyn-Swendsen-Wang representation and Monte Carlo algorithm, Physical Review D, vol.38, issue.6, pp.2009-2012, 1988.
DOI : 10.1103/PhysRevD.38.2009

A. Barbu and S. Zhu, Generalizing Swendsen???Wang for Image Analysis, Journal of Computational and Graphical Statistics, vol.16, issue.4, pp.877-900, 2007.
DOI : 10.1198/106186007X255144

A. Fouque, P. Ciuciu, and L. Risser, Multivariate Spatial Gaussian Mixture Modeling for statistical clustering of hemodynamic parameters in functional MRI, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.445-448, 2009.
DOI : 10.1109/ICASSP.2009.4959616

M. Jerrum and A. Sinclair, Polynomial-Time Approximation Algorithms for the Ising Model, SIAM Journal on Computing, vol.22, issue.5, pp.1087-1116, 1993.
DOI : 10.1137/0222066

O. Gruber, P. Indefrey, H. Steinmetz, and A. Kleinschmidt, Dissociating Neural Correlates of Cognitive Components in Mental Calculation, Cerebral Cortex, vol.11, issue.4, pp.350-359350, 2001.
DOI : 10.1093/cercor/11.4.350

T. Vincent, P. Ciuciu, and B. Thirion, Sensitivity analysis of parcellation in the joint detection-estimation of brain activity in fMRI, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.568-571, 2008.
DOI : 10.1109/ISBI.2008.4541059

P. Ciuciu, T. Vincent, A. Fouque, and A. Roche, Improved fMRI group studies based on spatially varying non-parametric BOLD signal modeling, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1263-1266, 2008.
DOI : 10.1109/ISBI.2008.4541233

H. Snoussi, J. Idier, ]. T. Veit, J. Idier, and S. Moussaoui, Bayesian blind separation of generalized hyperbolic processes in noisy and underdeterminate mixtures, IEEE Transactions on Signal Processing, vol.54, issue.9, pp.3257-3269, 2006.
DOI : 10.1109/TSP.2006.877660

URL : https://hal.archives-ouvertes.fr/hal-00400662